Efficiently predicting properties of porous crystalline materials has great potential to accelerate the high throughput screening process for developing new materials, as simulations carried out using first principles model are often computationally expensive. To effectively make use of Deep Learning methods to model these materials, we need to utilize the symmetries present in the crystals, which are defined by their space group. Existing methods for crystal property prediction either have symmetry constraints that are too restrictive or only incorporate symmetries between unit cells. In addition, these models do not explicitly model the porous structure of the crystal. In this paper, we develop a model which incorporates the symmetries of the unit cell of a crystal in its architecture and explicitly models the porous structure. We evaluate our model by predicting the heat of adsorption of CO$_2$ for different configurations of the mordenite zeolite. Our results confirm that our method performs better than existing methods for crystal property prediction and that the inclusion of pores results in a more efficient model.

翻译：高效预测多孔晶体材料的性质具有巨大潜力，可加速基于第一性原理模拟（计算成本通常较高）的新材料高通量筛选过程。为有效利用深度学习方法对这些材料进行建模，需利用晶体中由空间群定义的对称性。现有晶体性质预测方法要么施加了过于严格的对称约束，要么仅整合了晶胞间的对称性，且这些模型未能显式模拟晶体的多孔结构。本文提出一种新型模型，其架构中融合了晶体晶胞的对称性，并显式建模多孔结构。通过预测丝光沸石不同构型对CO$_2$的吸附热来评估模型性能。结果表明，我们的方法优于现有晶体性质预测方法，且引入孔隙结构使模型更高效。